Naturally occurring molecule rivals Ozempic in weight loss, sidesteps side effects
A naturally occurring molecule identified by Stanford Medicine researchers appears similar to semaglutide—also known as Ozempic—in suppressing appetite and reducing body weight. Notably, testing in animals also showed that it worked without some of the drug’s side effects, such as nausea, constipation, and significant loss of muscle mass.
The newly discovered molecule, BRP, acts through a separate but similar metabolic pathway and activates different neurons in the brain — seemingly offering a more targeted approach to body weight reduction.
“The receptors targeted by semaglutide are found in the brain but also in the gut, pancreas, and other tissues,” said assistant professor of pathology Katrin Svensson, PhD. “That’s why Ozempic has widespread effects including slowing the movement of food through the digestive tract and lowering blood sugar levels. In contrast, BRP appears to act specifically in the hypothalamus, which controls appetite and metabolism.”
Each prohormone can be divided in a variety of ways to create a plethora of functional peptide progeny. But with traditional methods of protein isolation, it’s difficult to pick peptide hormones (which are relatively rare) out of the biological soup of the much more numerous natural byproducts of protein degradation and processing.
The researchers focused on the prohormone convertase 1/3, which separates prohormones at specific amino acid sequences and is known to be involved in human obesity. One of the peptide products is glucagon-like peptide 1, or GLP-1, which regulates appetite and blood sugar levels; semaglutide works by mimicking the effect of GLP-1 in the body. The team turned to artificial intelligence to help them identify other peptides involved in energy metabolism.
Peptide Predictor
Instead of manually isolating proteins and peptides from tissues and using techniques like mass spectrometry to identify hundreds of thousands of peptides, the researchers designed a computer algorithm they named Peptide Predictor to identify typical prohormone convertase cleavage sites in all 20,000 human protein-coding genes. They then focused on genes that encode proteins that are secreted outside the cell — a key characteristic of hormones — and that have four or more possible cleavage sites. Doing so narrowed down the search to 373 prohormones, a manageable number to screen for their biological effects.
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